scholarly journals A Density-Based Algorithm for the Detection of Individual Trees from LiDAR Data

2021 ◽  
Vol 13 (2) ◽  
pp. 322
Author(s):  
Melissa Latella ◽  
Fabio Sola ◽  
Carlo Camporeale
Keyword(s):  
Point Clouds ◽  
Individual Tree ◽  
Tree Distribution ◽  
Tree Detection ◽  
Forest Modeling ◽  
Point Density ◽  
Potential Applications ◽  
Field Campaigns ◽  
The Individual ◽  
Individual Trees

Nowadays, LiDAR is widely used for individual tree detection, usually providing higher accuracy in coniferous stands than in deciduous ones, where the rounded-crown, the presence of understory vegetation, and the random spatial tree distribution may affect the identification algorithms. In this work, we propose a novel algorithm that aims to overcome these difficulties and yield the coordinates and the height of the individual trees on the basis of the point density features of the input point cloud. The algorithm was tested on twelve deciduous areas, assessing its performance on both regular-patterned plantations and stands with randomly distributed trees. For all cases, the algorithm provides high accuracy tree count (F-score > 0.7) and satisfying stem locations (position error around 1.0 m). In comparison to other common tools, the algorithm is weakly sensitive to the parameter setup and can be applied with little knowledge of the study site, thus reducing the effort and cost of field campaigns. Furthermore, it demonstrates to require just 2 points·m−2 as minimum point density, allowing for the analysis of low-density point clouds. Despite its simplicity, it may set the basis for more complex tools, such as those for crown segmentation or biomass computation, with potential applications in forest modeling and management.

Density-Based Individual Tree Detection from Three-Dimensional Point Clouds

2021 ◽  
Author(s):  
Carlo Camporeale ◽  
Melissa Latella ◽  
Fabio Sola
Keyword(s):  
Time Series ◽  
Spatial Distribution ◽  
Three Dimensional ◽  
Region Of Interest ◽  
Point Clouds ◽  
Individual Tree ◽  
Tree Detection ◽  
Round Shape ◽  
Point Density ◽  
Individual Trees

<p>The use of three-dimensional point clouds in forestry is steadily increasing. Numerous algorithms to detect individual trees from point clouds and derive some fundamental inventory parameters have been proposed so far, but they usually provide higher accuracy in coniferous stands than in deciduous one. In the latter kind of stands, indeed, the tree identification is hampered by the geometrical round shape of the crowns, the interlacing branches of adjacent trees and the usual presence of understory vegetation.</p><p>In an attempt to overcome these limitations, we developed an algorithm that is innovatively based on the areal point density of the three-dimensional cloud and that provides the height and coordinates of all the trees within a region of interest.</p><p>In this work, we apply the algorithm to different situations, ranging from the regularly-arranged plantations to the very interlaced crowns of the naturally established stands, demonstrating how it is able to correctly detect most of the trees and recreate a map of their spatial distribution. We also test its capability to deal with relatively low point density and explore the possibility to use it to recreate time series of vegetation biomass. Finally, we discuss the algorithm’s limitations and potentialities, particularly focusing on its coupling to other existing tools to deal with a wider range of applications in forestry and land management.</p><p> </p>

scholarly journals LiDAR-Based Wildfire Prevention in WUI: The Automatic Detection, Measurement and Evaluation of Forest Fuels

Forests ◽  
2019 ◽  
Vol 10 (2) ◽  
pp. 148 ◽  
Author(s):  
Marta Fernández-Álvarez ◽  
Julia Armesto ◽  
Juan Picos
Keyword(s):  
Point Clouds ◽  
Individual Tree ◽  
Forest Fuels ◽  
Gis Tools ◽  
Biomass Management ◽  
Measurement And Evaluation ◽  
The Individual ◽  
Individual Trees ◽  
Height Model

This paper describes a methodology using LiDAR point clouds with an ultra-high resolution in the characterization of forest fuels for further wildfire prevention and management. Biomass management strips were defined in three case studies using a particular Spanish framework. The data were acquired through a UAV platform. The proposed methodology allows for the detection, measurement and characterization of individual trees, as well as the analysis of shrubs. The individual tree segmentation process employed a canopy height model, and shrub cover LiDAR-derived models were used to characterize the vegetation in the strips. This way, the verification of the geometric legal restrictions was performed automatically and objectively using decision trees and GIS tools. As a result, priority areas, where wildfire prevention efforts should be concentrated in order to control wildfires, can be identified.

Assyrian sacred trees in the Brooklyn Museum

Iraq ◽  
1994 ◽  
Vol 56 ◽  
pp. 123-133 ◽  
Author(s):  
Pauline Albenda
Keyword(s):  
Present Author ◽  
Ninth Century ◽  
Palm Tree ◽  
The West ◽  
Individual Tree ◽  
Sacred Tree ◽  
The Individual ◽  
Individual Trees ◽  
Brooklyn Museum ◽  
Tree Type

The Brooklyn Museum houses twelve stone slabs with carved decoration from the Northwest Palace of Ashurnasirpal II. The motif of a stylized tree — the so-called Sacred Tree (see Figs. 1, 4, 6) — appears on seven of those slabs which come from rooms F, I, L, S, T of the ninth century palace at Nimrud. These tree renderings are representative of the sacred tree-type found in ten rooms of the royal residence and the west wing. Approximately 96 sacred trees, in two-register arrangement, appeared on the pictorial decorations in room I; the same motif occurred about 75 times in one-register arrangement on the reliefs of the other rooms. The abundance of the sacred tree motif on the wall decorations of the Northwest Palace attests to the significance of this plant. Its design deserves investigation; in Layard's words, “the tree, evidently a sacred symbol, is elaborately and tastefully formed.”In his study of the Ashurnasirpal II reliefs in American collections, Stearns did not attempt to list the sacred trees, because “variations in the sacred tree occur only in minor details,” and “the tree in itself is rarely useful in identifying the location of the reliefs.” These statements make clear Stearns' belief that the sacred trees were nearly alike. Other scholars, notably Weidner and Reade, have pointed out that on a number of slabs now in American and European museums are carvings of matching half trees, therefore indicating that when paired, these trees belonged to adjoining slabs originally. In trying to match half trees, one finds that individual sacred trees do differ in the rendering of specific details. Bleibtreu, in her analysis of the sacred tree-type, lists three variants of the flower found on the palmette-garland framing the individual tree on three sides. The present author, after examining the sacred trees carved on the slabs in The Brooklyn Museum, concludes that the design of the tree-type is more varied than heretofore presumed, and that its construction is more complex than indicated in previous descriptions of the subjects. An analysis of the Assyrian sacred tree-type may lead to possible conclusions regarding its intended image: a stylized palm tree, a cult object, an emblem of vegetation or “tree of life”, an imperial symbol, or a combination of those forms. In addition, one may consider to what extent the rendering of individual trees was the consequence of artistic inventiveness.

scholarly journals Automatic Extraction of Grasses and Individual Trees in Urban Areas Based on Airborne Hyperspectral and LiDAR Data

2020 ◽  
Vol 12 (17) ◽  
pp. 2725
Author(s):  
Qixia Man ◽  
Pinliang Dong ◽  
Xinming Yang ◽  
Quanyuan Wu ◽  
Rongqing Han
Keyword(s):  
Urban Areas ◽  
Three Dimensional ◽  
Hyperspectral Data ◽  
Classification Method ◽  
Urban Vegetation ◽  
Individual Tree ◽  
Cloud Data ◽  
Random Forest Classification ◽  
The Individual ◽  
Individual Trees

Urban vegetation extraction is very important for urban biodiversity assessment and protection. However, due to the diversity of vegetation types and vertical structure, it is still challenging to extract vertical information of urban vegetation accurately with single remotely sensed data. Airborne light detection and ranging (LiDAR) can provide elevation information with high-precision, whereas hyperspectral data can provide abundant spectral information on ground objects. The complementary advantages of LiDAR and hyperspectral data could extract urban vegetation much more accurately. Therefore, a three-dimensional (3D) vegetation extraction workflow is proposed to extract urban grasses and trees at individual tree level in urban areas using airborne LiDAR and hyperspectral data. The specific steps are as follows: (1) airborne hyperspectral and LiDAR data were processed to extract spectral and elevation parameters, (2) random forest classification method and object-based classification method were used to extract the two-dimensional distribution map of urban vegetation, (3) individual tree segmentation was conducted on a canopy height model (CHM) and point cloud data separately to obtain three-dimensional characteristics of urban trees, and (4) the spatial distribution of urban vegetation and the individual tree delineation were assessed by validation samples and manual delineation results. The results showed that (1) both the random forest classification method and object-based classification method could extract urban vegetation accurately, with accuracies above 99%; (2) the watershed segmentation method based on the CHM could extract individual trees correctly, except for the small trees and the large tree groups; and (3) the individual tree segmentation based on point cloud data could delineate individual trees in three-dimensional space, which is much better than CHM segmentation as it can preserve the understory trees. All the results suggest that two- and three-dimensional urban vegetation extraction could play a significant role in spatial layout optimization and scientific management of urban vegetation.

PointCNN-Based Individual Tree Detection Using LiDAR Point Clouds

2021 ◽  
pp. 89-100
Author(s):  
Wenyuan Ying ◽  
Tianyang Dong ◽  
Zhanfeng Ding ◽  
Xinpeng Zhang
Keyword(s):  
Point Clouds ◽  
Individual Tree ◽  
Tree Detection

Development and evaluation of an automated tree detection–delineation algorithm for monitoring regenerating coniferous forests

2005 ◽  
Vol 35 (10) ◽  
pp. 2332-2345 ◽  
Author(s):  
D A Pouliot ◽  
D J King ◽  
D G Pitt
Keyword(s):  
Absolute Error ◽  
Tree Regeneration ◽  
Tree Size ◽  
Detection Accuracy ◽  
Individual Tree ◽  
Commission Errors ◽  
Tree Detection ◽  
Crown Diameter ◽  
Accuracy Index ◽  
The Individual

An algorithm is presented for automated detection–delineation of coniferous tree regeneration that combines strategies of several existing algorithms, including image processing to isolate conifer crowns, optimal image scale determination, initial crown detection, and crown boundary segmentation and refinement. The algorithm is evaluated using 6-cm pixel airborne imagery in operational regeneration conditions typically encountered in the boreal forest 5–10 years after harvest. Detection omission and commission errors as well as an accuracy index combining both error types were assessed on a tree by tree basis, on an aggregated basis for each study area, in relation to tree size and the amount of woody competition present. Delineation error was assessed in a similar manner using field-measured crown diameters as a reference. The individual tree detection accuracy index improved with increasing tree size and was >70% for trees larger than 30 cm crown diameter. Crown diameter absolute error measured from automated delineations was <23%. Large crown diameters tended to be slightly underestimated. The presence of overtopping woody competition had a negligible effect on detection accuracy and only reduced estimates of crown diameter slightly.

scholarly journals Comparison of Classical Methods and Mask R-CNN for Automatic Tree Detection and Mapping Using UAV Imagery

2022 ◽  
Vol 14 (2) ◽  
pp. 295
Author(s):  
Kunyong Yu ◽  
Zhenbang Hao ◽  
Christopher J. Post ◽  
Elena A. Mikhailova ◽  
Lili Lin ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Plantation Forest ◽  
Remote Sensing Images ◽  
Individual Tree ◽  
Tree Detection ◽  
Band Combination ◽  
Individual Trees ◽  
Lm Algorithm ◽  
Multi Band

Detecting and mapping individual trees accurately and automatically from remote sensing images is of great significance for precision forest management. Many algorithms, including classical methods and deep learning techniques, have been developed and applied for tree crown detection from remote sensing images. However, few studies have evaluated the accuracy of different individual tree detection (ITD) algorithms and their data and processing requirements. This study explored the accuracy of ITD using local maxima (LM) algorithm, marker-controlled watershed segmentation (MCWS), and Mask Region-based Convolutional Neural Networks (Mask R-CNN) in a young plantation forest with different test images. Manually delineated tree crowns from UAV imagery were used for accuracy assessment of the three methods, followed by an evaluation of the data processing and application requirements for three methods to detect individual trees. Overall, Mask R-CNN can best use the information in multi-band input images for detecting individual trees. The results showed that the Mask R-CNN model with the multi-band combination produced higher accuracy than the model with a single-band image, and the RGB band combination achieved the highest accuracy for ITD (F1 score = 94.68%). Moreover, the Mask R-CNN models with multi-band images are capable of providing higher accuracies for ITD than the LM and MCWS algorithms. The LM algorithm and MCWS algorithm also achieved promising accuracies for ITD when the canopy height model (CHM) was used as the test image (F1 score = 87.86% for LM algorithm, F1 score = 85.92% for MCWS algorithm). The LM and MCWS algorithms are easy to use and lower computer computational requirements, but they are unable to identify tree species and are limited by algorithm parameters, which need to be adjusted for each classification. It is highlighted that the application of deep learning with its end-to-end-learning approach is very efficient and capable of deriving the information from multi-layer images, but an additional training set is needed for model training, robust computer resources are required, and a large number of accurate training samples are necessary. This study provides valuable information for forestry practitioners to select an optimal approach for detecting individual trees.

scholarly journals INDIVIDUAL TREE OF URBAN FOREST EXTRACTION FROM VERY HIGH DENSITY LIDAR DATA

2016 ◽  
Vol XLI-B3 ◽  
pp. 337-343 ◽  
Author(s):  
A. Moradi ◽  
M. Satari ◽  
M. Momeni
Keyword(s):  
Urban Forest ◽  
Accuracy Assessment ◽  
Airborne Lidar ◽  
Light Detection And Ranging ◽  
Individual Tree ◽  
Point Density ◽  
Peak Points ◽  
3D Information ◽  
Individual Trees ◽  
Very High

Airborne LiDAR (Light Detection and Ranging) data have a high potential to provide 3D information from trees. Most proposed methods to extract individual trees detect points of tree top or bottom firstly and then using them as starting points in a segmentation algorithm. Hence, in these methods, the number and the locations of detected peak points heavily effect on the process of detecting individual trees. In this study, a new method is presented to extract individual tree segments using LiDAR points with 10cm point density. In this method, a two-step strategy is performed for the extraction of individual tree LiDAR points: finding deterministic segments of individual trees points and allocation of other LiDAR points based on these segments. This research is performed on two study areas in Zeebrugge, Bruges, Belgium (51.33° N, 3.20° E). The accuracy assessment of this method showed that it could correctly classified 74.51% of trees with 21.57% and 3.92% under- and over-segmentation errors respectively.

scholarly journals The Feasibility of Modelling the Crown Profile of Larix olgensis Using Unmanned Aerial Vehicle Laser Scanning Data

Sensors ◽  
2020 ◽  
Vol 20 (19) ◽  
pp. 5555 ◽  
Author(s):  
Ying Quan ◽  
Mingze Li ◽  
Zhen Zhen ◽  
Yuanshuo Hao ◽  
Bin Wang
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Laser Scanning ◽  
Low Cost ◽  
Pearson Correlation ◽  
Point Clouds ◽  
Larix Olgensis ◽  
Individual Tree ◽  
Profile Fitting ◽  
Aerial Vehicle ◽  
Individual Trees

Unmanned aerial vehicle (UAV) laser scanning, as an emerging form of near-ground light detection and ranging (LiDAR) remote sensing technology, is widely used for crown structure extraction due to its flexibility, convenience, and high point density. Herein, we evaluated the feasibility of using a low-cost UAV-LiDAR system to extract the fine-scale crown profile of Larix olgensis. Specifically, individual trees were isolated from LiDAR point clouds and then stratified from the point clouds of segmented individual tree crowns at 0.5 m intervals to obtain the width percentiles of each layer as profile points. Four equations (the parabola, Mitscherlich, power, and modified beta equations) were then applied to model the profiles of the entire and upper crown. The results showed that a region-based hierarchical cross-section analysis algorithm can successfully delineate 77.4% of the field-measured trees in high-density (>2400 trees/ha) forest stands. The crown profile generated with the 95th width percentile was adequate when compared with the predicted value of the existing field-based crown profile model (the Pearson correlation coefficient (ρ) was 0.864, root mean square error (RMSE) = 0.3354 m). The modified beta equation yielded slightly better results than the other equations for crown profile fitting and explained 85.9% of the variability in the crown radius for the entire crown and 87.8% of this variability for the upper crown. Compared with the cone and 3D convex hull volumes, the crown volumes predicted by our profile models had significantly smaller errors. The results revealed that the crown profile can be well described by using UAV-LiDAR, providing a novel way to obtain crown profile information without destructive sampling and showing the potential of the use of UAV-LiDAR in future forestry investigations and monitoring.

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